

'''simple docstring'''
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class 					_snake_case		:






			@property
			def 		UpperCamelCase__							(  self     ):
				return self.get_dummy_input()






			@property
			def 		UpperCamelCase__							(  self     ):
				if self.block_type == "down":
					return (4, 32, 16, 16)
				elif self.block_type == "mid":
					return (4, 32, 32, 32)
				elif self.block_type == "up":
					return (4, 32, 64, 64)

				raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''     )






			def 		UpperCamelCase__							(  self			,_snake_case=True			,_snake_case=False			,_snake_case=False			,_snake_case=False			,):
				UpperCAmelCase_     :      List[Any]						=  4
				UpperCAmelCase_     :      Any						=  32
				UpperCAmelCase_     :      Tuple						=  (32, 32)

				UpperCAmelCase_     :      Optional[int]						=  torch.manual_seed(0     )
				UpperCAmelCase_     :      str						=  torch.device(_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  (batch_size, num_channels) + sizes
				UpperCAmelCase_     :      Tuple						=  randn_tensor(_snake_case			,generator=_snake_case			,device=_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  {"hidden_states": hidden_states}

				if include_temb:
					UpperCAmelCase_     :      Optional[Any]						=  1_28
					UpperCAmelCase_     :      Any						=  randn_tensor((batch_size, temb_channels)			,generator=_snake_case			,device=_snake_case     )

				if include_res_hidden_states_tuple:
					UpperCAmelCase_     :      Optional[Any]						=  torch.manual_seed(1     )
					UpperCAmelCase_     :      List[str]						=  (randn_tensor(_snake_case			,generator=_snake_case			,device=_snake_case     ),)

				if include_encoder_hidden_states:
					UpperCAmelCase_     :      int						=  floats_tensor((batch_size, 32, 32)     ).to(_snake_case     )

				if include_skip_sample:
					UpperCAmelCase_     :      str						=  randn_tensor(((batch_size, 3) + sizes)			,generator=_snake_case			,device=_snake_case     )

				return dummy_input






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  {
				    "in_channels": 32,
				    "out_channels": 32,
				    "temb_channels": 1_28,
				}
				if self.block_type == "up":
					UpperCAmelCase_     :      List[str]						=  32

				if self.block_type == "mid":
					init_dict.pop("out_channels"     )

				UpperCAmelCase_     :      List[Any]						=  self.dummy_input
				return init_dict, inputs_dict






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  self.prepare_init_args_and_inputs_for_common()
				UpperCAmelCase_     :      Tuple						=  self.block_class(**_snake_case     )
				unet_block.to(_snake_case     )
				unet_block.eval()

				with torch.no_grad():
					UpperCAmelCase_     :      Any						=  unet_block(**_snake_case     )

				if isinstance(_snake_case			,_snake_case     ):
					UpperCAmelCase_     :      List[Any]						=  output[0]

				self.assertEqual(output.shape			,self.output_shape     )

				UpperCAmelCase_     :      Union[str, Any]						=  output[0, -1, -3:, -3:]
				UpperCAmelCase_     :      Tuple						=  torch.tensor(_snake_case     ).to(_snake_case     )
				assert torch_all_close(output_slice.flatten()			,_snake_case			,atol=5E-3     )






			@unittest.skipIf(torch_device == "mps"			,"Training is not supported in mps"     )
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  self.prepare_init_args_and_inputs_for_common()
				UpperCAmelCase_     :      Tuple						=  self.block_class(**_snake_case     )
				model.to(_snake_case     )
				model.train()
				UpperCAmelCase_     :      List[Any]						=  model(**_snake_case     )

				if isinstance(_snake_case			,_snake_case     ):
					UpperCAmelCase_     :      Optional[Any]						=  output[0]

				UpperCAmelCase_     :      int						=  torch.device(_snake_case     )
				UpperCAmelCase_     :      List[str]						=  randn_tensor(output.shape			,device=_snake_case     )
				UpperCAmelCase_     :      str						=  torch.nn.functional.mse_loss(_snake_case			,_snake_case     )
				loss.backward()












"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")